Recent time has witnessed tremendous growth in communication technology worldwide. Consequently, communication devices have started replacing manual human efforts in a big way. Particularly, communication devices such as smart phone and applications embedded therein for physiological sensing are rapidly gaining popularity in both developed and developing nations. Smart phone based physiological sensing applications provides both elderly as well as young adults with an opportunity to monitor numerous physiological vitals regularly at home for indicative and preventive measurements without possessing dedicated clinical devices.
In order to support various physiological sensing applications, smart phones of recent time are equipped with a plurality of inbuilt sensors such as accelerometer, microphone and camera. Accelerometer and microphone can be employed to measure certain physiological parameters such as breathing rate and heart rate, while smart phone camera may be utilized for estimating several vitals using photoplethysmography, which is a non-invasive technique to measure the instantaneous blood flow in capillaries. Since, capillary blood flow increases during systole and reduces during diastole. Thus, photoplethysmogram (PPG) signal of a person is periodic in nature, whose fundamental frequency indicates the heart rate. The photoplethysmogram (PPG) is further used for measuring several physiological vitals including heart rate, blood pressure, respiratory rate, blood oxygen saturation and certain ECG parameters.
Prior art literature illustrates a variety of solutions for estimating systolic (Ps) and diastolic (Pd) blood pressure from photoplethysmogram. Some of the prior art literature uses a combination of PPG and ECG signal for measuring the pulse transit time to estimate blood pressure. In another approach, photoplethysmogram signal, synchronized with a microphone can also be used to serve this purpose. It is important to observe that some of the prior art literature proposes a set of time domain photoplethysmogram features to estimate Ps and Pd using machine learning techniques; an indirect approach of estimating blood pressure could be via the R and C parameters of Windkesel model using photoplethysmogram features.
A majority of existing solutions used for measuring blood pressure using photoplethysmogram can be employed only when they are applied on clean and noise-free photoplethysmogram signal. However, such solution exhibit practical constraints when photoplethysmogram signals are captured using communication devices such as smart phones. Smart phones typically capture video at 30 fps, yielding a very low sampling rate of the extracted photoplethysmogram signal, which is 30 Hz compared to a clinical devices on 100 Hz or more. In addition to that, surrounding lights while capturing photoplethysmogram signals using smart phone also affect the signal quality. A little finger movement or even a variation in finger pressure can largely affect the photoplethysmogram signal quality, thereby the signal in time domain becomes more vulnerable and less reliable. Thus, photoplethysmogram signals captured using smart phones are noisy in nature. Although they have been successfully used to determine heart rate using frequency domain analysis, further indirect markers like blood pressure require time domain analysis for which the signal needs to be substantially cleaned. The existing prior art solution illustrates use of filters for noise cleaning of captured photoplethysmogram signals, which might not be sufficient for detailed noise cleaning and remove the undesired frequency. In turn, such filtering might not be enough to estimate vitals such as blood pressure.
Thus, in the light of the above mentioned background art, it is evident that, a method and system for noise cleaning of photoplethysmogram signals for estimating blood pressure of a user is desired.